#NC 12-2-24: this code is now in a separate combining script, just loading the file now
#combined_all = read.xlsx(here::here("graphs/combined/combined_all.xlsx"))
combined_all = read.xlsx(here::here("graphs/combined/master_aggregated_CATE.xlsx")) #specifying axis maximums (x and y both) and breaks by datasets
#update this when adding new datasets/outcomes
#TODO: may need to change these depending on future simulation results
scenario_maximums = rbind(
data.frame(dataset = "asap", outcome = "X16BTMCRET", max = 1.2, max_bias = 1.2, max_se = 1.2, breaks = I(list(c(0,.2,.4,.6,.8, 1))),max_agg = .35, max_bias_agg = .35, max_se_agg = .35, breaks_agg = I(list(c(0,.1,.2,.3)))),
data.frame(dataset = "asap", outcome = "C16BMVDEG", max = 1, max_bias = 1, max_se = 1, breaks = I(list(c(0,.2,.4,.6,.8))),max_agg = 1, max_bias_agg = 1, max_se_agg = 1, breaks_agg = I(list(c(0,.2,.4,.6,.8)))))all_scenarios = combined_all
df = combined_all
all_scenarios$set_id = paste( df$dataset, df$outcome, df$cov_set_size, df$train_set_size, sep="-" )
ALL_MODELS <- unique( all_scenarios$model )
TYPE_SHAPE_MAP <- c(
"ATE" = 20, "OLS S" = 18,
"INF" = 2, "RF" = 3, "CDML" = 8,
"LASSO" = 10, "SL" = 16,
"XGBOOST" = 1, "BART" = 0
)
TYPE_COLOR_MAP <- c(
"ATE" = "black", "OLS S" = "black",
"INF" = "darkgrey", "RF" = "#E69F00", "CDML" = "#F0E442",
"LASSO" = "#009E73", "SL" = "#D55E00",
"XGBOOST" = "#CC79A7", "BART" = "#0072B2" # "#56B4E9"
)
LEGEND_COLORS = setdiff( names(TYPE_COLOR_MAP), c("LASSO INF", "RF INF", "ATE", "OLS S") )
# For individual methods
SHAPE_MAP <- c(
"ATE" = 0, "OLS S" = 1,
"RF INF" = 2, "RF T" = 3, "RF MOM IPW" = 4, "RF MOM DR" = 5,
"CF" = 6, "CF LC" = 7, "CDML" = 8, "LASSO INF" = 9, "LASSO T" = 10, "LASSO MOM IPW" = 11,
"LASSO MOM DR" = 12, "LASSO MCM" = 13, "LASSO MCM EA" = 14, "LASSO R" = 15,
"SL T" = 16, "SL S" = 17, "XGBOOST S" = 18, "XGBOOST R" = 19, "BART T" = 20, "BART S" = 21
)
old_type_shape_map <- c(
"ATE" = 18, "OLS S" = 20, "INF" = 2, "RF" = 3, "CDML" = 8, "LASSO" = 10, "SL" = 16, "XGBOOST" = 0, "BART" = 1
)
old_shape_map <- c(
"ATE" = 0, "OLS S" = 1, "RF INF" = 2, "RF T" = 3, "RF MOM IPW" = 4, "RF MOM DR" = 5,
"CF" = 6, "CF LC" = 7, "CDML" = 8, "LASSO INF" = 9, "LASSO T" = 10, "LASSO MOM IPW" = 11,
"LASSO MOM DR" = 12, "LASSO MCM" = 13, "LASSO MCM EA" = 14, "LASSO R" = 15,
"SL T" = 16, "SL S" = 17, "XGBOOST S" = 18, "XGBOOST R" = 19, "BART T" = 20, "BART S" = 21
)contour_onequeen(scen_dataset="asap", scen_outcome = "X16BTMCRET", scen_train_set_size = "1000", scen_cov_set_size = "small", plotqueen = "RF T")## Scale for shape is already present.
## Adding another scale for shape, which will replace the existing scale.
scenario_plot(scen_dataset="asap", scen_outcome = "X16BTMCRET", scen_train_set_size = "1000", scen_cov_set_size = "small")stack_miniplots(scen_dataset= "asap", scen_outcome = "X16BTMCRET"
# , scen_train_set_size = "2000", scen_cov_set_size = "small"
)## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).
## ATE and CDML queens only
scenario_allqueens(scen_dataset="asap", scen_outcome = "X16BTMCRET", scen_train_set_size = "1000", scen_cov_set_size = "small")## Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in
## dplyr 1.1.0.
## ℹ Please use `reframe()` instead.
## ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()`
## always returns an ungrouped data frame and adjust accordingly.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## TODO: fix binary scenario_allqueens (shown: asap_1_small_1000)
scenario_allqueens(scen_dataset="asap", scen_outcome = "C16BMVDEG", scen_train_set_size = "2000", scen_cov_set_size = "small")# Moving to large covariate set plot ----
scenario_trail(scen_dataset="asap", scen_outcome = "X16BTMCRET",
additional_filter_var = "cov_set_size",
additional_filter_val = "small",
trail_var = "train_set_size",
trail_val_from = 1000,
trail_val_to = 5000)